Add new GIS example: Solar Adoption model

[ShreyasN707]

Does it belong?

• No significant overlap with an existing example, or if there is, the overlap is justified
• The model is well-scoped: the simplest model that demonstrates its idea
• It showcases Mesa features not already well-covered by other examples
• It showcases interesting ABM mechanics, dynamics, or phenomena

Is it correct and current?

• Uses current Mesa APIs (no deprecated schedulers, manual unique_id, dict portrayals, etc.)
• Runs and visualizes out of the box
• Deterministic with a fixed rng seed

Is it clean?

• Minimal: no dead code, unused imports, or unnecessary complexity
• Clear naming; logic readable without deep domain knowledge
• README explains what it does, what it demonstrates, and how to run it (concise, links to references for theory)
• PR follows the template, commits are clean, and there are no unrelated changes

What this example demonstrates

• Using GeoSpace to manage spatial relationships between agents

• Loading environmental data with RasterLayer.from_file()

• Creating agents from GeoJSON using AgentCreator

• Visualizing the simulation with a Solara dashboard using make_geospace_component, where households change color as they adopt solar

Implementation notes

A data/generate_data.py script generates synthetic households.geojson and solar_radiation.tif files so the example can run without external datasets.

The code follows the standard Mesa example structure and uses the built-in random generator.

The example runs successfully with the existing test_gis_examples.py test suite.

[EwoutH]

Thanks for the PR, looks like a cool model.

Could you:

• Checkout the test failure
• Request a peer-review (and maybe do one or two yourself)

[ShreyasN707]

since no one was ready to give a peer review to my PR i reviewed it myself

• There is no overlap with other examples
• It showcases interesting ABM mechanics, dynamics or phenomena
• It showcases Mesa features not already well-covered by other examples
• Uses current Mesa APIs
• I tested the running and visualizatoin of it
• There are no dead code, unused imports, or unnecessary complexity
• The README explains what it does, what it demonstrates, and how to run it
• This PR follows the template and commits are clean

[chadac]

This suppresses an error which leads to uniform economic viability.

[chadac]

This should preferably be under gis/solar_adoption/solar_adoption. Then the app.py should import these with import solar_adoption.agents for example.

[chadac]

Should be able to import without this hack.

[chadac]

Shouldn't need this dependency

[chadac]

Personal opinion here: Since you re-evaluate at each step, these probabilities are a bit too high. Ends up with total adoption even with extremely low social + economic weights in a short number of steps. It may help visually to pad this a bit more.

[chadac]

Adding two probabilities together doesn't make a ton of sense from a generative perspective IMO. Might be worth structuring it as a mixture model where each agent chooses to make an economic vs. social vs. noop decision, and then subsequently evaluating the social vs. economic probability. That way you're guaranteed to stay within 0 - 1 and you have a more proper probabilistic model to describe the "solar adoption" decision.